I have a set of time series for a given quantity (e.g. CPU). The measurements are roughly evenly spaced, but the data points aren't synchronised between sets and some sets have missing measurements. The measurements often exhibit a strong pattern over the course of an hour or day.
I'd like to compare the most recent time series to a set of previous ones for similar periods (e.g. the same hour from previous days), to see if the recent behaviour is similar to past behaviour. I want this for a data visualisation, rather than any measure of correlation.
At the moment I'm taking a time slice from each previous set, combining them and calculating percentiles on each slice. The lack of synchronisation of measurements means that each time slice can have a varying number of measurements.
Is there a better method to achieve this?
Here's some sample data to illustrate the problem, though I need to be able to do this on other sets. The sample includes 9 sets of hourly CPU data for each of two machines. The
mapped_date column aligns all series to the same hour.